Smart engine risk assessments

ABSTRACT

Systems, methods, and computer-readable medium storing instructions can be used to predict insurance information. One of the methods includes obtaining information about an insured entity. The method includes providing the information to a machine learning system, the machine learning system trained to provide insurance information based on the provided information. The method includes in response to providing the information, receiving a prediction of the insurance information. The method also includes adjusting an insurance premium based on the prediction.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityunder 35 U.S.C. § 120 to U.S. application Ser. No. 16/259,220, filed onJan. 28, 2019, which claims to U.S. Provisional Application Ser. No.62/623,862, filed on Jan. 30, 2018, the entire contents of each of whichare incorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to systems and methods for predictinginsurance information using one or more machine learning techniques.

BACKGROUND

Insurance is a means of protection from financial loss. It is a form ofrisk management primarily used to hedge against the risk of acontingent, uncertain loss. Typical insurance transactions can involvethe insured assuming a guaranteed and known relatively small loss in theform of payment to the insurer in exchange for the insurer's promise tocompensate the insured in the event of a covered loss. The loss may ormay not be financial, but it is generally reducible to financial terms,and involves something in which the insured has an insurable interestestablished by ownership, possession, or preexisting relationship.

SUMMARY

Implementations of the present disclosure are generally directed topredicting insurance information. More specifically, implementations aredirected to using machine learning techniques, such as deep learningthat includes classification, clustering, and/or other techniques, todetermine metric(s) for insurance information that can be used to betterallocate costs when compared to conventional technologies used fordetermining insurance information metrics.

In general, innovative aspects of the subject matter described in thisspecification can be embodied in methods that includes act of obtaininginformation about an insured entity. The method includes the act ofproviding the information about the insured entity to a machine learningsystem, the machine learning system trained to predict insuranceinformation based on the provided information. The method includes theact of, in response to providing the information, receiving predictedinsurance information. The method also includes the act of adjusting aninsurance premium based on the predicted insurance information.

Implementations can include one or more of the following features. Theinformation about the insured entity can include information collectedfrom at least one of: an Internet of Things device or a sensor. Theinformation about the insured entity can include information collectedfrom financial transactions. The information about the insured entitycan include information collected from social media. The predictedinsurance information can include at least one of: a predicted number ofclaims or a predicted total value of claims over a predetermined timeperiod. The methods may include the act of providing a recommendation ofat least one action that could be taken to alter the predicted insuranceinformation.

Other implementations of any of the above aspects include correspondingsystems, apparatus, and computer programs that are configured to performthe actions of the methods, encoded on computer storage devices. Thepresent disclosure also provides a computer-readable storage mediumcoupled to one or more processors and having instructions stored thereonwhich, when executed by the one or more processors, cause the one ormore processors to perform operations in accordance with implementationsof the methods provided herein. The present disclosure further providesa system for implementing the methods provided herein. The systemincludes one or more processors, and a computer-readable storage mediumcoupled to the one or more processors having instructions stored thereonwhich, when executed by the one or more processors, cause the one ormore processors to perform operations in accordance with implementationsof the methods provided herein.

Implementations of the present disclosure provide one or more of thefollowing advantages. By implementing machine learning techniques, theamount of information (data) about an insured entity that can be used topredict insurance information, such as predicting a number of filedclaims and/or predicting a total value of filed claims over apredetermined time period, can substantially increase when compared withsome conventional technologies used for predicting such insuranceinformation. Thus, the systems and methods described herein can moreaccurately allocate risks then some conventional technologies. This canallow for a reduction in insurance premiums and costs associated withinsuring entities.

It is appreciated that aspects and features in accordance with thepresent disclosure can include any combination of the aspects andfeatures described herein. That is, aspects and features in accordancewith the present disclosure are not limited to the combinations ofaspects and features specifically described herein, but also include anycombination of the aspects and features provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts an example environment for allocating risk anddetermining insurance premiums, according to implementations of thepresent disclosure.

FIG. 2 depicts an example of training data used to generate the model(s)for determining likely claims, according to implementations of thepresent disclosure.

FIG. 3 depicts a flow diagram of an example process for determininginsurance premiums using a machine learning system, according toimplementations of the present disclosure.

FIG. 4 depicts an example computing system, according to implementationsof the present disclosure.

DETAILED DESCRIPTION

Insurance premiums are generally determined based on an estimate of therisk associated with an insurance claim. In some conventionaltechnologies for estimating risk, the estimation of risk can tend to begeneral. One difference between providing insurance and selling othertypes of goods is that the actual cost of providing the insurance isunknown until the policy period has lapsed. Therefore, insurance ratesare generally based on predictions rather than actual costs. Someconventional technologies determine rates by statistical analysis ofpast losses based on a limited number of variables. The variables tendto be ones that have the most predictive power, but they are far fromperfect. As a consequence, some customers are charged too much for theirinsurance, while other customers are charged too little.

Today, more information is available about customers than ever before.If a customer buys an item with a credit card, there is a record of thepurchase. Smart devices and sensors can provide a constant stream ofinformation. Some devices monitor a customer's heart rate, exercisehabits, etc. Other devices monitor the home, for example, when doors arelocked or unlocked, the temperature of the water, electricity usage,temperature, and humidity information. Some sensors can be used todetect damage to the home. Devices in automobiles can rate the driver'sdriving habits, driving routes, frequency of use, accident detection,etc. Individuals also provide additional information about theirlifestyle (for example, people post information on social networkingsites describing their daily activities, eating habits, vacations,lifestyle, etc.)

This plethora of information is generally underused in the insuranceindustry. As a result, some users, who exhibit behaviors that reduce thelikelihood and severity of a claim, subsidize other users, who do notexhibit these behaviors. The increased capabilities of machine learningcan make it possible utilize this plethora or information to moreaccurately allocate risk and set premiums accordingly. For example, theinformation can be used with historic claims and payment amounts (orlosses) in order to more accurately allocate risk (and thereforeinsurance premiums) across a base of insurance customers.

Implementations provide techniques for allocating risk and determininginsurance premiums. One or more models may be developed using machinelearning (ML), as described further below. In some implementations, oneor more models may be trained, through supervised ML, using trainingdata from historic activities taken by users and historic claims placedby those users. Training data may include, for each of one or moredifferent individuals, information about those individuals, claims, andclaim loss amounts. The models may be trained to output, based ontraining data. Accordingly, some implementations provide predictedclaims and losses for users based on the additional information, andthus provide for a more complete allocation of risk across insuredindividuals. Moreover, once the models are sufficiently trained and/orrefined to provide accurate a mechanism to more accurately allocaterisks and set premiums across a broad range of individuals.

FIG. 1 depicts an example environment 100 for allocating risk anddetermining insurance premiums, according to implementations of thepresent disclosure. As shown in FIG. 1 , information about a user 102may be collected. The information can include, for example, informationabout life events 104. Life events can include, but are not limited to,events such as an engagement, marriage, birth of a child, change ofresidence, enlistment in the military, discharge from the military,retirement, start of a new job, etc. The information can also includeinformation about user behavior 106. User behavior can include, forexample, driving history data, credit history data, daily exercise data,lifestyle data, etc.

The information can also include information about purchases 108.Purchases may refer to any goods purchased by the user, including butnot limited to, an automobile, jewelry, home appliances, home systems,furniture, etc. Purchases may also refer to services engaged by theuser. These services can include, but are not limited to, snow removalservices, home repair services, contractors, automobile service,automobile repair, etc.

The information can also include financial information 110. Financialinformation can include, for example, information about assets andliabilities including savings, checking, investment accounts, retirementaccounts, revolving credit accounts, mortgages, etc.

The collected information can be provided to a machine learning system112. The machine learning system 112 can include a trained learningmachine 114. The trained learning machine 114 can accept the collectedinformation and produce a report 116 using one or more trained machinelearning models 120. The report 116 can include an estimate of theclaimable activities that are likely to occur. In some implementations,the report includes a projected likelihood that a user will have a claimand a projected amount of the losses for any claims. In someimplementations, the report includes any of the above informationsegmented by time periods (for example, the likelihood that the userwill have a claim in the next three months, the next six months, and thenext year, the next five years, ten years (or any other duration oftime); the amount of losses in anticipated claims in the next threemonths, the next six months, and the next year, the next five years, thenext ten years, (or any other duration of time); etc . . . ).

In some implementations, the environment may include a premiumidentification engine 118. The premium identification engine 118 mayidentify an appropriate premium for a particular type of insurance (forexample, auto, personal article, home, renter, flood, etc.) based on thereport 116.

FIG. 2 depicts an example of training data 202 used to generate themodel 216 for determining likely claims, according to implementations ofthe present disclosure. As shown in the example of FIG. 2 , the trainingdata 202 may include any suitable number of sets of training data. Eachset of training data 202 include historic information about a user(including life event information 204, user behavior information 206,purchase information 208, and/or financial information 210). The set oftraining data may also include report information 212 associated withany claims and losses experienced by the user over one or more differenttimeframes.

Implementations are not limited to the particular example of trainingdata 202 shown in FIG. 2 , and may include less information, moreinformation, differently formatted information, and so forth. As shownin the example, in some implementations, financial information may notbe used.

In some implementations, the applied machine learning techniques includefeature extraction to build different neurons within a neural network,where each neuron corresponds to a feature of the session records. Theneural network can be one of several types of neural networks. In someimplementations, the neural network is a convolutional neural network(CNN), a recurrent neural network, a radial basis function neuralnetwork, a Kohonen self-organizing neural network, and/or a modularneural network. One or more features may translate to one or more surveyquestions. For example, a particular feature may correspond to a pieceor type of information, such that the strength of a feature present in auser information leads to a particular metric being determined for thecorresponding report. Features may correspond to negative and/orpositive influences on the report. A feature may correspond to one ormore neurons that each indicates a characteristic of the report. In someinstances, a feature may correspond to a particular relationship, orinterrelationship, between multiple neurons. In some implementations,each feature is associated with a different neural network, orsub-network, of multiple neurons, and the neural network may bedeveloped based on supervised learning using the training data (e.g.,labeled data). Unsupervised learning may also be employed.

Implementations may employ feature engineering, learning curves, anomalyfiltering, training algorithm(s), recurrent neural network(s), and/orother techniques.

In some implementations, a large sample of historical data (e.g., userinformation and associated claims and losses) may be employed to developthe model(s) 216. Such large sample may provide for the development ofmodel(s) 216 that take into account a wide variance, among thehistorical information. A goal of the modeling may be to provideestimates or predictions of features, corresponding to claims and losseswith at least a threshold degree of accuracy (e.g., at least 90%accurate).

Implementations may also employ unlabeled data to develop the model(s)216. In some implementations, clustering techniques may be applied todevelop the model(s) 216 based on unlabeled data. Clustering maydetermine sets (e.g., clusters) of individuals that are similar in someway with respect to their characteristics, such as purchase history,financial information, and so forth. Clustering may be described asself-classification without the use of labeled data.

FIG. 3 depicts a flow diagram of an example process 300 for determininginsurance premiums based using a machine learning system, according toimplementations of the present disclosure. Operations of the process maybe performed by the machine learning system 112, the trained learningmachine 114, the trained model(s) 120, and/or other software module(s)executing on the machine learning system 112 and/or elsewhere.

The process 300 obtains 302 information about an insured entity. Theinformation can be collected from at different sources, including,Internet of Things devices (networked physical devices, vehicles, homeappliances, and other items embedded with electronics, software,sensors, actuators, and network connectivity which enable these objectsto connect and exchange data), sensors, financial transactions, and/orsocial media. The information may be obtained using information known bythe insurance company, for example, the insurance company may include abank or other financial institution. Obtaining the information caninclude obtaining customer records from accounts held by the bank of theanother financial institution. In some implementations, obtaining theinformation may include obtaining information about a customer stored ona block chain. In some implementations, the block chain data may beshared across multiple institutions. I

The process 300 provides 304 the information about the insured entity toa machine learning system, the machine learning system trained toprovide insurance information based on the provided information aboutthe insured entity.

In response to providing the information, the process 300 receives 306predicted insurance information from the machine learning system. Insome implementations, the predicted insurance information includes apredicted number of claims and/or a predicted total value of claims overa predetermined time period. In some implementations, the predictedinsurance information is based on the information about the insuredentity. For example, based on information about the insured entitygathered from social media (such as recent skydiving trips, recentengagement photos, etc.) and bank transaction information (e.g., recentalcohol/tobacco purchases, recent gym membership purchases, etc.) themachine learning system can predict a number of claims the insuredentity might file and/or a total value of claims an insured entity mayfile over a predetermined time period.

The process 300 adjusts 306 an insurance premium based on theprediction.

FIG. 4 depicts an example computing system, according to implementationsof the present disclosure. The system 400 may be used for any of theoperations described with respect to the various implementationsdiscussed herein. The system 400 may include one or more processors 410,a memory 420, one or more storage devices 430, and one or moreinput/output (I/O) devices 450 controllable through one or more I/Ointerfaces 440. The various components 410, 420, 430, 440, or 450 may beinterconnected through at least one system bus 460, which may enable thetransfer of data between the various modules and components of thesystem 400.

The processor(s) 410 may be configured to process instructions forexecution within the system 400. The processor(s) 410 may includesingle-threaded processor(s), multi-threaded processor(s), or both. Theprocessor(s) 410 may be configured to process instructions stored in thememory 420 or on the storage device(s) 430. The processor(s) 410 mayinclude hardware-based processor(s) each including one or more cores.The processor(s) 410 may include general purpose processor(s), specialpurpose processor(s), or both.

The memory 420 may store information within the system 400. In someimplementations, the memory 420 includes one or more computer-readablemedia. The memory 420 may include any number of volatile memory units,any number of non-volatile memory units, or both volatile andnon-volatile memory units. The memory 420 may include read-only memory,random access memory, or both. In some examples, the memory 420 may beemployed as active or physical memory by one or more executing softwaremodules.

The storage device(s) 430 may be configured to provide (e.g.,persistent) mass storage for the system 400. In some implementations,the storage device(s) 430 may include one or more computer-readablemedia. For example, the storage device(s) 430 may include a floppy diskdevice, a hard disk device, an optical disk device, or a tape device.The storage device(s) 430 may include read-only memory, random accessmemory, or both. The storage device(s) 430 may include one or more of aninternal hard drive, an external hard drive, or a removable drive.

One or both of the memory 420 or the storage device(s) 430 may includeone or more computer-readable storage media (CRSM). The CRSM may includeone or more of an electronic storage medium, a magnetic storage medium,an optical storage medium, a magneto-optical storage medium, a quantumstorage medium, a mechanical computer storage medium, and so forth. TheCRSM may provide storage of computer-readable instructions describingdata structures, processes, applications, programs, other modules, orother data for the operation of the system 400. In some implementations,the CRSM may include a data store that provides storage ofcomputer-readable instructions or other information in a non-transitoryformat. The CRSM may be incorporated into the system 400 or may beexternal with respect to the system 400. The CRSM may include read-onlymemory, random access memory, or both. One or more CRSM suitable fortangibly embodying computer program instructions and data may includeany type of non-volatile memory, including but not limited to:semiconductor memory devices, such as EPROM, EEPROM, and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. In some examples,the processor(s) 410 and the memory 420 may be supplemented by, orincorporated into, one or more application-specific integrated circuits(ASICs).

The system 400 may include one or more I/O devices 450. The I/Odevice(s) 450 may include one or more input devices such as a keyboard,a mouse, a pen, a game controller, a touch input device, an audio inputdevice (e.g., a microphone), a gestural input device, a haptic inputdevice, an image or video capture device (e.g., a camera), or otherdevices. In some examples, the I/O device(s) 450 may also include one ormore output devices such as a display, LED(s), an audio output device(e.g., a speaker), a printer, a haptic output device, and so forth. TheI/O device(s) 450 may be physically incorporated in one or morecomputing devices of the system 400, or may be external with respect toone or more computing devices of the system 400.

The system 400 may include one or more I/O interfaces 440 to enablecomponents or modules of the system 400 to control, interface with, orotherwise communicate with the I/O device(s) 450. The I/O interface(s)440 may enable information to be transferred in or out of the system400, or between components of the system 400, through serialcommunication, parallel communication, or other types of communication.For example, the I/O interface(s) 440 may comply with a version of theRS-232 standard for serial ports, or with a version of the IEEE 1284standard for parallel ports. As another example, the I/O interface(s)440 may be configured to provide a connection over Universal Serial Bus(USB) or Ethernet. In some examples, the I/O interface(s) 440 may beconfigured to provide a serial connection that is compliant with aversion of the IEEE 1394 standard.

The I/O interface(s) 440 may also include one or more network interfacesthat enable communications between computing devices in the system 400,or between the system 400 and other network-connected computing systems.The network interface(s) may include one or more network interfacecontrollers (NICs) or other types of transceiver devices configured tosend and receive communications over one or more networks using anynetwork protocol.

Computing devices of the system 400 may communicate with one another, orwith other computing devices, using one or more networks. Such networksmay include public networks such as the internet, private networks suchas an institutional or personal intranet, or any combination of privateand public networks. The networks may include any type of wired orwireless network, including but not limited to local area networks(LANs), wide area networks (WANs), wireless WANs (WWANs), wireless LANs(WLANs), mobile communications networks (e.g., 3G, 4G, Edge, etc.), andso forth. In some implementations, the communications between computingdevices may be encrypted or otherwise secured. For example,communications may employ one or more public or private cryptographickeys, ciphers, digital certificates, or other credentials supported by asecurity protocol, such as any version of the Secure Sockets Layer (SSL)or the Transport Layer Security (TLS) protocol.

The system 400 may include any number of computing devices of any type.The computing device(s) may include, but are not limited to: a personalcomputer, a smartphone, a tablet computer, a wearable computer, animplanted computer, a mobile gaming device, an electronic book reader,an automotive computer, a desktop computer, a laptop computer, anotebook computer, a game console, a home entertainment device, anetwork computer, a server computer, a mainframe computer, a distributedcomputing device (e.g., a cloud computing device), a microcomputer, asystem on a chip (SoC), a system in a package (SiP), and so forth.Although examples herein may describe computing device(s) as physicaldevice(s), implementations are not so limited. In some examples, acomputing device may include one or more of a virtual computingenvironment, a hypervisor, an emulation, or a virtual machine executingon one or more physical computing devices. In some examples, two or morecomputing devices may include a cluster, cloud, farm, or other groupingof multiple devices that coordinate operations to provide loadbalancing, failover support, parallel processing capabilities, sharedstorage resources, shared networking capabilities, or other aspects.

Implementations and all of the functional operations described in thisspecification may be realized in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Implementations may be realized asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium may be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “computing system” encompasses allapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, or multiple processorsor computers. The apparatus may include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them. A propagated signal is anartificially generated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) may be written in any appropriate form ofprogramming language, including compiled or interpreted languages, andit may be deployed in any appropriate form, including as a standaloneprogram or as a module, component, subroutine, or other unit suitablefor use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program may bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program may be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any appropriate kind of digital computer.Generally, a processor may receive instructions and data from a readonly memory or a random access memory or both. Elements of a computercan include a processor for performing instructions and one or morememory devices for storing instructions and data. Generally, a computermay also include, or be operatively coupled to receive data from ortransfer data to, or both, one or more mass storage devices for storingdata, e.g., magnetic, magneto optical disks, or optical disks. However,a computer need not have such devices. Moreover, a computer may beembedded in another device, e.g., a mobile telephone, a personal digitalassistant (PDA), a mobile audio player, a Global Positioning System(GPS) receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory may be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, implementations may be realizedon a computer having a display device, e.g., a CRT (cathode ray tube) orLCD (liquid crystal display) monitor, for displaying information to theuser and a keyboard and a pointing device, e.g., a mouse or a trackball,by which the user may provide input to the computer. Other kinds ofdevices may be used to provide for interaction with a user as well; forexample, feedback provided to the user may be any appropriate form ofsensory feedback, e.g., visual feedback, auditory feedback, or tactilefeedback; and input from the user may be received in any appropriateform, including acoustic, speech, or tactile input.

Implementations may be realized in a computing system that includes aback end component, e.g., as a data server, or that includes amiddleware component, e.g., an application server, or that includes afront end component, e.g., a client computer having a graphical UI or aweb browser through which a user may interact with an implementation, orany appropriate combination of one or more such back end, middleware, orfront end components. The components of the system may be interconnectedby any appropriate form or medium of digital data communication, e.g., acommunication network. Examples of communication networks include alocal area network (“LAN”) and a wide area network (“WAN”), e.g., theInternet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations may also beimplemented in combination in a single implementation. Conversely,various features that are described in the context of a singleimplementation may also be implemented in multiple implementationsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination may in some examples be excised from the combination, andthe claimed combination may be directed to a sub-combination orvariation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemsmay generally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, various formsof the flows shown above may be used, with steps re-ordered, added, orremoved. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A method comprising: obtaining, by one or moreprocessors, first sensor data from one or more first sensors regarding ahome of a first insured entity; training, by the one or more processors,a machine learning system comprising a neural network to predict, basedon the first sensor data, a total value of claims filed by the firstinsured entity over a time period, wherein training the machine learningsystem comprises: generating a report regarding the first insured entitybased on the first sensor data, extracting a plurality of features fromthe report, and generating a plurality of neurons for the neuralnetwork, wherein each of the neurons represents at least one of (i) oneor more of the features of the report, or (ii) a relationship betweentwo or more other neurons; obtaining, by the one or more processors,second sensor data from one or more second sensors regarding a home of asecond insured entity; processing, by the one or more processors, thesecond sensor data using the trained machine learning system includingthe plurality of neurons to predict a corresponding total value ofclaims filed by the second insured entity over the time period; anddetermining, by the one or more processors, an insurance premium basedon the predicted corresponding total value of claims filed by the secondinsured entity over the time period.
 2. The method of claim 1, whereindetermining the insurance premium comprises determining an adjustment tothe insurance premium.
 3. The method of claim 1, wherein the firstsensor data comprises at least one of: a lock state of a door in thehome of the first insured entity; a temperature of water in the home ofthe first insured entity; an electricity usage of the home of the firstinsured entity, a temperature in the home of the first insured entity,and a humidity in the home of the first insured entity.
 4. The method ofclaim 1, wherein the second sensor data comprises at least one of: alock state of a door in the home of the second insured entity; atemperature of water in the home of the second insured entity; anelectricity usage of the home of the second insured entity, atemperature in the home of the second insured entity, and a humidity inthe home of the second insured entity.
 5. The method of claim 1, whereintraining the machine learning system further comprises: clusteringunlabeled data into a plurality of clusters, wherein the unlabeled datacomprises the first sensor data.
 6. The method of claim 5, wherein theunlabeled data further comprises additional sensor data obtained fromone or more additional sensors regarding one or more additional homes ofone or more additional insured entities.
 7. The method of claim 1,wherein the neural network comprises at least one of: a convolutionneural network, a recurrent neural network, a radial basis functionneural network, a Kohonen self-organizing neural network, or a modularneural network.
 8. A system comprising: at least one processor; and amemory communicatively coupled to the at least one processor, the memorystoring instructions which, when executed by the at least one processor,cause the at least one processor to perform operations comprising:obtaining first sensor data from one or more first sensors regarding ahome of a first insured entity; training a machine learning systemcomprising a neural network to predict, based on the first sensor data,a total value of claims filed by the first insured entity over a timeperiod, wherein training the machine learning system comprises:generating a report regarding the first insured entity based on thefirst sensor data, extracting a plurality of features from the report,and generating a plurality of neurons for the neural network, whereineach of the neurons represents at least one of (i) one or more of thefeatures of the report, or (ii) a relationship between two or more otherneurons; obtaining second sensor data from one or more second sensorsregarding a home of a second insured entity; processing the secondsensor data using the trained machine learning system including theplurality of neurons to predict a corresponding total value of claimsfiled by the second insured entity over the time period; and determiningan insurance premium based on the predicted corresponding total value ofclaims filed by the second insured entity over the time period.
 9. Thesystem of claim 8, wherein determining the insurance premium comprisesdetermining an adjustment to the insurance premium.
 10. The system ofclaim 8, wherein the first sensor data comprises at least one of: a lockstate of a door in the home of the first insured entity; a temperatureof water in the home of the first insured entity; an electricity usageof the home of the first insured entity, a temperature in the home ofthe first insured entity, and a humidity in the home of the firstinsured entity.
 11. The system of claim 8, wherein the second sensordata comprises at least one of: a lock state of a door in the home ofthe second insured entity; a temperature of water in the home of thesecond insured entity; an electricity usage of the home of the secondinsured entity, a temperature in the home of the second insured entity,and a humidity in the home of the second insured entity.
 12. The systemof claim 8, wherein training the machine learning system furthercomprises: clustering unlabeled data into a plurality of clusters,wherein the unlabeled data comprises the first sensor data.
 13. Thesystem of claim 12, wherein the unlabeled data further comprisesadditional sensor data obtained from one or more additional sensorsregarding one or more additional homes of one or more additional insuredentities.
 14. The system of claim 8, wherein the neural networkcomprises at least one of: a convolution neural network, a recurrentneural network, a radial basis function neural network, a Kohonenself-organizing neural network, or a modular neural network.
 15. One ormore non-transitory computer-readable media storing instructions which,when executed by at least one processor, cause the at least oneprocessor to perform operations comprising: obtaining first sensor datafrom one or more first sensors regarding a home of a first insuredentity; training a machine learning system comprising a neural networkto predict, based on the first sensor data, a total value of claimsfiled by the first insured entity over a time period, wherein trainingthe machine learning system comprises: generating a report regarding thefirst insured entity based on the first sensor data, extracting aplurality of features from the report, and generating a plurality ofneurons for the neural network, wherein each of the neurons representsat least one of (i) one or more of the features of the report, or (ii) arelationship between two or more other neurons; obtaining second sensordata from one or more second sensors regarding a home of a secondinsured entity; processing the second sensor data using the trainedmachine learning system including the plurality of neurons to predict acorresponding total value of claims filed by the second insured entityover the time period; and determining an insurance premium based on thepredicted corresponding total value of claims filed by the secondinsured entity over the time period.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein determining the insurancepremium comprises determining an adjustment to the insurance premium.17. The one or more non-transitory computer-readable media of claim 15,wherein the first sensor data comprises at least one of: a lock state ofa door in the home of the first insured entity; a temperature of waterin the home of the first insured entity; an electricity usage of thehome of the first insured entity, a temperature in the home of the firstinsured entity, and a humidity in the home of the first insured entity.18. The one or more non-transitory computer-readable media of claim 15,wherein the second sensor data comprises at least one of: a lock stateof a door in the home of the second insured entity; a temperature ofwater in the home of the second insured entity; an electricity usage ofthe home of the second insured entity, a temperature in the home of thesecond insured entity, and a humidity in the home of the second insuredentity.
 19. The one or more non-transitory computer-readable media ofclaim 15, wherein training the machine learning system furthercomprises: clustering unlabeled data into a plurality of clusters,wherein the unlabeled data comprises the first sensor data.
 20. The oneor more non-transitory computer-readable media of claim 19, wherein theunlabeled data further comprises additional sensor data obtained fromone or more additional sensors regarding one or more additional homes ofone or more additional insured entities.
 21. The one or morenon-transitory computer-readable media of claim 15, wherein the neuralnetwork comprises at least one of: a convolution neural network, arecurrent neural network, a radial basis function neural network, aKohonen self-organizing neural network, or a modular neural network.